In　this　paper,　the　specific　values　of　the　ink　transferring　ratio　was　predicted　according　to　the　data　of　its　influential　factors　by　using　neural　network.　RBF　neural　network　was　selected　as　the　model　of　prediction　network　by　comparing　the　radial　basis　function　(RBF)　neural　network,　Elman　neural　network　and　back　propagation　(BP)　neural　network.　The　main　influence　factors　of　ink　transferring　ratio　were　determined　according　to　the　correlation　among　the　various　factors　and　their　impact　on　the　ink　transferring　ratio.　Considering　the　main　influencing　factors,　a　group　of　experiments　were　taken　by　using　orthogonal　experimental　design　and　uniform　design　experimentation,　and　the　result　was　used　as　the　sample　data　of　the　neural　network.　The　RBF　neural　network　was　trained　by　the　sample　data,　which　enable　the　network　to　predict　the　ink　transferring　ratio　under　different　factors.　Results　show　that　the　predicted　value　of　RBF　neural　network　is　more　accurate　than　Elman　neural　network　and　BP　neural　network　through　contrastive　analysis.　©　2016,　Beijing　University　of　Technology.　All　right　reserved.